1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
  knitr::kable(.,format = "html", format.args = list(decimal.mark = ",", big.mark = "."),
                   caption="Tabla 1. Gastos Casa (últimos 30 registros)", align =rep('c', 3)) %>%
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
    kableExtra::scroll_box(width = "100%", height = "300px")
Tabla 1. Gastos Casa (últimos 30 registros)
fecha gasto monto gastador obs
18/5/2022 Comida 41.970 Tami NA
19/5/2022 VTR 21.990 Andrés NA
21/5/2022 Pila estufa 6.414 Andrés Pila cr2 switchbot
25/5/2022 Reloj 8.914 Andrés Reloj alarma temperatura y humedad
28/5/2022 Comida 138.918 Tami Wild Foods
29/5/2022 Parafina 42.490 Tami NA
30/5/2022 Comida 50.346 Tami NA
30/5/2022 Netflix 8.320 Tami NA
1/6/2022 Diosi 7.000 Andrés Pilas collar
3/6/2022 Electricidad 24.792 Andrés Pac enel 01686518
6/6/2022 Enceres 19.400 Tami Caja Papel Higiénico
7/6/2022 Comida 15.260 Andrés NA
7/6/2022 Comida 23.450 Andrés NA
13/6/2022 Comida 57.775 Tami NA
18/6/2022 Gas 81.350 Andrés NA
19/6/2022 VTR 21.990 Andrés NA
20/6/2022 Electricidad 67.655 Andrés NA
21/6/2022 Comida 38.000 Andrés NA
21/6/2022 Comida 15.000 Andrés Flor de loto verduras
24/6/2022 Comida 40.400 Andrés Bar la Providencia
27/6/2022 Agua 12.502 Andrés PAC AGUAS ANDIN 000000005687837
29/6/2022 Netflix 8.320 Tami NA
29/6/2022 Comida 68.213 Tami NA
30/6/2022 Comida 15.310 Tami NA
30/6/2022 Electricidad 67.655 Andrés NA
2/7/2022 Diosi 35.990 Andrés NA
3/7/2022 Gas 19.600 Andrés NA
3/7/2022 Parafina 44.029 Tami NA
31/3/2019 Comida 9.000 Andrés NA
8/9/2019 Comida 24.588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 4.1966e+08   2    4.6221 0.0103 *  
## lag_depvar    7.3925e+10   1 1628.3998 <2e-16 ***
## Residuals     2.0883e+10 460                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr     p adj
## 1-0  7228.838   821.1395 13636.54 0.0224588
## 2-0 27964.529 22025.2426 33903.82 0.0000000
## 2-1 20735.691 17107.1037 24364.28 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
## 30   28331.57             0   28706.00
## 31   25617.86             0   28331.57
## 32   27223.29             0   25617.86
## 33   31622.57             0   27223.29
## 34   32021.43             0   31622.57
## 35   33634.57             0   32021.43
## 36   30784.86             0   33634.57
## 37   34770.57             0   30784.86
## 38   38443.00             1   34770.57
## 39   35073.00             1   38443.00
## 40   31422.29             1   35073.00
## 41   30103.29             1   31422.29
## 42   19319.29             1   30103.29
## 43   27926.29             1   19319.29
## 44   30715.43             1   27926.29
## 45   31962.29             1   30715.43
## 46   39790.14             1   31962.29
## 47   39211.57             1   39790.14
## 48   44548.57             1   39211.57
## 49   49398.00             1   44548.57
## 50   41039.00             1   49398.00
## 51   34821.29             1   41039.00
## 52   29123.57             1   34821.29
## 53   21275.71             1   29123.57
## 54   28476.14             1   21275.71
## 55   24561.86             1   28476.14
## 56   20323.57             1   24561.86
## 57   25370.00             1   20323.57
## 58   26811.86             1   25370.00
## 59   27151.86             1   26811.86
## 60   27623.29             1   27151.86
## 61   22896.57             1   27623.29
## 62   41889.29             1   22896.57
## 63   44000.14             1   41889.29
## 64   38558.00             1   44000.14
## 65   43373.86             1   38558.00
## 66   49001.00             1   43373.86
## 67   61213.29             1   49001.00
## 68   58939.57             1   61213.29
## 69   42046.86             1   58939.57
## 70   39191.71             1   42046.86
## 71   42646.43             1   39191.71
## 72   36121.57             1   42646.43
## 73   30915.57             1   36121.57
## 74   20273.43             1   30915.57
## 75   23938.29             1   20273.43
## 76   19274.29             1   23938.29
## 77   21662.29             1   19274.29
## 78   15819.00             1   21662.29
## 79   18126.14             1   15819.00
## 80   17240.71             1   18126.14
## 81   16127.71             1   17240.71
## 82   13917.14             1   16127.71
## 83   15379.86             1   13917.14
## 84   19510.14             1   15379.86
## 85   24567.29             1   19510.14
## 86   25700.43             1   24567.29
## 87   25729.00             1   25700.43
## 88   26435.00             1   25729.00
## 89   31157.14             1   26435.00
## 90   29818.43             1   31157.14
## 91   30962.43             1   29818.43
## 92   28746.71             1   30962.43
## 93   27830.71             1   28746.71
## 94   28252.14             1   27830.71
## 95   28717.57             1   28252.14
## 96   21365.43             1   28717.57
## 97   24816.86             1   21365.43
## 98   16838.57             1   24816.86
## 99   15529.14             1   16838.57
## 100  13286.29             1   15529.14
## 101  13629.43             1   13286.29
## 102  14404.86             1   13629.43
## 103  19524.86             1   14404.86
## 104  18475.71             1   19524.86
## 105  22495.00             1   18475.71
## 106  22254.57             1   22495.00
## 107  24173.29             1   22254.57
## 108  27466.43             1   24173.29
## 109  24602.43             1   27466.43
## 110  20531.14             1   24602.43
## 111  20846.43             1   20531.14
## 112  23875.71             1   20846.43
## 113  36312.71             1   23875.71
## 114  34244.00             1   36312.71
## 115  36347.43             1   34244.00
## 116  39779.71             1   36347.43
## 117  42018.71             1   39779.71
## 118  39372.57             1   42018.71
## 119  33444.00             1   39372.57
## 120  29255.86             1   33444.00
## 121  31640.14             1   29255.86
## 122  29671.14             1   31640.14
## 123  31023.71             1   29671.14
## 124  39723.43             1   31023.71
## 125  39314.14             1   39723.43
## 126  38239.86             1   39314.14
## 127  34649.43             1   38239.86
## 128  36688.43             1   34649.43
## 129  42867.57             1   36688.43
## 130  42226.86             1   42867.57
## 131  32155.14             1   42226.86
## 132  33603.00             1   32155.14
## 133  37254.43             1   33603.00
## 134  33145.57             1   37254.43
## 135  31299.43             1   33145.57
## 136  30252.00             1   31299.43
## 137  26310.71             1   30252.00
## 138  27929.86             1   26310.71
## 139  27666.14             1   27929.86
## 140  25017.57             1   27666.14
## 141  27335.00             1   25017.57
## 142  25760.71             1   27335.00
## 143  18436.86             1   25760.71
## 144  21906.00             1   18436.86
## 145  19418.14             1   21906.00
## 146  22826.14             1   19418.14
## 147  23444.29             1   22826.14
## 148  25264.86             1   23444.29
## 149  25473.29             1   25264.86
## 150  27366.86             1   25473.29
## 151  28855.86             1   27366.86
## 152  32326.86             1   28855.86
## 153  27141.43             1   32326.86
## 154  26297.71             1   27141.43
## 155  23499.14             1   26297.71
## 156  30246.29             1   23499.14
## 157  39931.86             1   30246.29
## 158  38020.43             2   39931.86
## 159  35004.00             2   38020.43
## 160  40750.86             2   35004.00
## 161  42363.29             2   40750.86
## 162  46273.57             2   42363.29
## 163  41083.29             2   46273.57
## 164  35711.29             2   41083.29
## 165  41921.71             2   35711.29
## 166  60583.29             2   41921.71
## 167  63115.57             2   60583.29
## 168  61300.14             2   63115.57
## 169  57666.43             2   61300.14
## 170  55834.00             2   57666.43
## 171  58927.71             2   55834.00
## 172  57810.57             2   58927.71
## 173  48987.14             2   57810.57
## 174  52219.29             2   48987.14
## 175  56503.57             2   52219.29
## 176  56545.00             2   56503.57
## 177  64705.57             2   56545.00
## 178  53833.29             2   64705.57
## 179  50114.00             2   53833.29
## 180  39592.43             2   50114.00
## 181  29907.29             2   39592.43
## 182  33923.29             2   29907.29
## 183  45489.00             2   33923.29
## 184  44866.29             2   45489.00
## 185  51680.57             2   44866.29
## 186  58257.00             2   51680.57
## 187  70600.57             2   58257.00
## 188  76648.00             2   70600.57
## 189  69430.14             2   76648.00
## 190  69651.57             2   69430.14
## 191  77745.14             2   69651.57
## 192  72795.86             2   77745.14
## 193  67670.71             2   72795.86
## 194  55357.86             2   67670.71
## 195  48524.00             2   55357.86
## 196  50154.43             2   48524.00
## 197  45111.57             2   50154.43
## 198  36147.00             2   45111.57
## 199  43501.57             2   36147.00
## 200  41472.43             2   43501.57
## 201  41058.00             2   41472.43
## 202  41605.57             2   41058.00
## 203  49382.86             2   41605.57
## 204  59558.57             2   49382.86
## 205  59134.57             2   59558.57
## 206  61109.00             2   59134.57
## 207  63004.43             2   61109.00
## 208  67344.29             2   63004.43
## 209  78180.86             2   67344.29
## 210  69117.86             2   78180.86
## 211  55597.57             2   69117.86
## 212  49426.14             2   55597.57
## 213  39119.43             2   49426.14
## 214  35636.86             2   39119.43
## 215  39201.14             2   35636.86
## 216  27777.00             2   39201.14
## 217  47207.00             2   27777.00
## 218  55587.29             2   47207.00
## 219  56619.71             2   55587.29
## 220  82679.86             2   56619.71
## 221  91259.57             2   82679.86
## 222  93552.71             2   91259.57
## 223 102242.71             2   93552.71
## 224  91884.00             2  102242.71
## 225  85013.86             2   91884.00
## 226  84535.29             2   85013.86
## 227  80700.43             2   84535.29
## 228  79740.57             2   80700.43
## 229  85163.14             2   79740.57
## 230  86724.86             2   85163.14
## 231  80355.00             2   86724.86
## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   308 50198.79 16479.064
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2073.932251   4063.959746   -561.451127   2417.803844  -3015.862883 
##             7             8             9            10            11 
##    501.942310  -5680.554827  -1159.802629  -3935.211434   -357.629982 
##            12            13            14            15            16 
##  -4887.974435  -1521.455103   -812.347756    457.552636  -3181.537709 
##            17            18            19            20            21 
##   -297.948779  -2061.573476   6679.617400  -1532.261697  -1201.242710 
##            22            23            24            25            26 
##   1488.409566  -1194.748820    233.960071   1687.103707  -7130.356511 
##            27            28            29            30            31 
##    986.577149   8212.430041    351.623560    -81.118195  -2464.012447 
##            32            33            34            35            36 
##   1539.072504   4519.908945   1031.851570   2292.590941  -1982.388444 
##            37            38            39            40            41 
##   4521.142722   4252.571958  -2362.140220  -3035.347979  -1128.821091 
##            42            43            44            45            46 
## -10747.440940   7387.579945   2572.153463   1354.710587   8080.928181 
##            47            48            49            50            51 
##    586.185912   6434.372917   6568.385366  -6075.245264  -4907.506417 
##            52            53            54            55            56 
##  -5111.664363  -7925.402375   6208.867699  -4067.234949  -4847.119695 
##            57            58            59            60            61 
##   3943.974566    927.145060     -6.783442    164.243877  -4978.993153 
##            62            63            64            65            66 
##  18189.929630   3520.095158  -3787.059842   5837.110617   7209.284313 
##            67            68            69            70            71 
##  14449.803089   1386.130222 -13497.682246  -1427.552985   4549.774502 
##            72            73            74            75            76 
##  -5027.437042  -4468.509823 -10510.979393   2556.563154  -5345.459416 
##            77            78            79            80            81 
##   1163.338934  -6789.823808    680.055355  -2243.810284  -2574.504838 
##            82            83            84            85            86 
##  -3801.703933   -385.876634   2452.052602   3859.951343    524.941125 
##            87            88            89            90            91 
##   -447.656661    233.099537   4331.468029  -1179.415745   1147.382630 
##            92            93            94            95            96 
##  -2079.093515  -1037.436617    193.308268    286.390750  -7476.973653 
##            97            98            99           100           101 
##   2470.316444  -8557.420634  -2817.769744  -3903.703410  -1578.922042 
##           102           103           104           105           106 
##  -1106.671541   3328.211654  -2244.620644   2701.617505  -1089.983009 
##           107           108           109           110           111 
##   1041.157876   2639.053164  -3134.547533  -4675.394466   -762.993089 
##           112           113           114           115           116 
##   1987.727263  11748.253083  -1308.962292   2622.243813   4196.080774 
##           117           118           119           120           121 
##   3402.542758  -1221.830697  -4812.447340  -3762.501140   2322.147390 
##           122           123           124           125           126 
##  -1753.447944   1338.800147   8843.472814    747.701620     35.033379 
##           127           128           129           130           131 
##  -2606.228211   2605.034253   6982.653132    882.461640  -8623.160371 
##           132           133           134           135           136 
##   1723.389656   4095.588526  -3239.426599  -1455.258193   -871.558454 
##           137           138           139           140           141 
##  -3887.406362   1213.992857   -480.287727  -2895.858857   1761.670223 
##           142           143           144           145           146 
##  -1860.140327  -7793.063942   2146.949076  -3406.010588   2200.093530 
##           147           148           149           150           151 
##   -192.844396   1081.577361   -318.529175   1390.888713   1206.855691 
##           152           153           154           155           156 
##   3362.274914  -4889.897027  -1152.113563  -3205.235499   6014.537831 
##           157           158           159           160           161 
##   9738.785256  -3142.223002  -4469.841174   3942.130437    477.020537 
##           162           163           164           165           166 
##   2962.672246  -5682.480303  -6468.691108   4488.077244  17662.529503 
##           167           168           169           170           171 
##   3706.699275   -346.087528  -2375.810591   -997.732337   3714.993233 
##           172           173           174           175           176 
##   -135.548578  -7971.944465   3055.989537   4484.570073    740.690432 
##           177           178           179           180           181 
##   8864.658346  -9217.762290  -3331.023769 -10566.483184 -10955.469484 
##           182           183           184           185           186 
##   1617.674750   9635.120131  -1206.285481   6158.188912   6713.970544 
##           187           188           189           190           191 
##  13247.049690   8388.524407  -4172.436012   2426.208212  10324.140170 
##           192           193           194           195           196 
##  -1776.083750  -2528.368887 -10312.992704  -6268.033081   1400.334436 
##           197           198           199           200           201 
##  -5083.060307  -9592.100562   5682.966396  -2844.183710  -1465.797414 
##           202           203           204           205           206 
##   -552.064628   6741.423608  10045.648584    631.068258   2980.114862 
##           207           208           209           210           211 
##   3131.070449   5796.253721  12798.417741  -5839.051740 -11351.877025 
##           212           213           214           215           216 
##  -5577.685728 -10431.738634  -4807.986902   1833.265922 -12740.041320 
##           217           218           219           220           221 
##  16783.566964   7996.804663   1624.973470  26772.931517  12327.647156 
##           222           223           224           225           226 
##   7040.328505  13704.260902  -4332.355970  -2050.231946   3541.195200 
##           227           228           229           230           231 
##    129.171752   2557.537806   8828.174785   5598.867760  -2150.815638 
##           232           233           234           235           236 
##  -2002.693191   9310.799087 -11691.872306  -7303.447623  -8463.445653 
##           237           238           239           240           241 
##  -9924.747859   3357.886995   1582.476048  -8091.944790  -8703.113823 
##           242           243           244           245           246 
##   9459.945759  -7525.457918   2798.524315 -10037.758997  -3694.870927 
##           247           248           249           250           251 
##   1798.305956   1333.922482 -12019.875639   4052.630914   2398.449246 
##           252           253           254           255           256 
##   4500.263307   2357.984940   -972.174753  11332.746041  20943.804807 
##           257           258           259           260           261 
##   3035.427526  -4434.944467   4026.932804  -1797.934618   3679.784337 
##           262           263           264           265           266 
##  -4928.555877 -10892.652254  -4592.583012   -334.938873  -5003.448663 
##           267           268           269           270           271 
##   9012.491613  -4158.293060   4357.968700  -1992.036678   4568.860672 
##           272           273           274           275           276 
##    793.987730   7382.881944  -1411.289302  12054.301678  -4683.362623 
##           277           278           279           280           281 
##   1701.533741   -400.750002   7843.001327  -5142.202376  -2734.640151 
##           282           283           284           285           286 
## -11219.040121  -2487.522470  18860.156052   7761.884594   2637.455738 
##           287           288           289           290           291 
##   -732.811702    834.324057   6337.363702   6766.924989 -18941.609644 
##           292           293           294           295           296 
## -11051.911728  -7896.817403   9975.575908   3247.506184  -1045.570841 
##           297           298           299           300           301 
##  27548.727011   9882.358299   4633.443995   9238.172274   2510.974680 
##           302           303           304           305           306 
##  -1356.053861   7638.937339 -24601.426854  -3493.986933    -79.036719 
##           307           308           309           310           311 
##  -6863.730160  -3777.500623   3169.739879  -9000.422200  -2930.528623 
##           312           313           314           315           316 
##  -7863.617354   1969.166746  -2795.693912   2418.302816  -3764.159048 
##           317           318           319           320           321 
##  27792.832809   -758.961429   3283.072713  10796.677634   5440.158207 
##           322           323           324           325           326 
##  32195.302766   4565.751997 -21461.400828   1608.049957    943.489142 
##           327           328           329           330           331 
##  -6608.232842  -1762.401942 -33252.934350   1362.899547  -1863.603223 
##           332           333           334           335           336 
##    347.567697  -2753.415191   4514.987924    -86.559410  -6614.726801 
##           337           338           339           340           341 
##  -2707.340046  -1767.791357  -7254.475084   4347.285824   -960.759799 
##           342           343           344           345           346 
##  -1335.882931   -594.711079    563.912456    843.306440  -1283.776870 
##           347           348           349           350           351 
##  -9109.172462 -12770.682890   2887.488296  -3822.076753  -3139.361532 
##           352           353           354           355           356 
##  -5453.303502   2313.622512   1879.007327   3191.548813  -3396.195716 
##           357           358           359           360           361 
##   -121.791107   1051.523076   7354.348348    504.895327    178.874183 
##           362           363           364           365           366 
##   2794.618543  -2577.549487   -668.074005  -8525.701748  -4295.857973 
##           367           368           369           370           371 
##  -5837.028846  -4513.166513  -6779.325217   5550.790560    797.600176 
##           372           373           374           375           376 
##   7511.510536  -7365.142703  -1900.737964  -3016.016306  -2071.460393 
##           377           378           379           380           381 
## -12053.182121   2447.239567 -10157.569059   6279.322246   9794.890290 
##           382           383           384           385           386 
##   3425.274969  -2157.499892   1868.866378   6974.982352  11542.293276 
##           387           388           389           390           391 
##  -5823.042505  -5289.299604     -4.834606   8717.422672   1852.604171 
##           392           393           394           395           396 
##  11246.813057  -9992.419281   2820.214876    731.837515    585.902394 
##           397           398           399           400           401 
##   -624.198623   -511.507371 -14417.195597   8804.222908  -1023.930930 
##           402           403           404           405           406 
##  -1196.345204   7177.253104  -7834.420266  -1080.143453  -2298.985927 
##           407           408           409           410           411 
##  -5555.086605  -2521.431409  -3555.187824  -8357.756974   6628.255484 
##           412           413           414           415           416 
##   2015.350365  -7043.485499  -7277.674302  14715.020351   4074.550437 
##           417           418           419           420           421 
##   4683.885490  -7911.515442  -4503.450420  -2300.373914   3144.524104 
##           422           423           424           425           426 
## -13740.581770  -2337.078668  -8635.611980   3570.489027   7449.920255 
##           427           428           429           430           431 
##   6916.946832  -3756.986422  -3841.103880  -4396.219254  -1414.182764 
##           432           433           434           435           436 
##  -5333.233385  -6191.416017  -5451.997218   -850.067071   -327.070354 
##           437           438           439           440           441 
##  -4481.921847   3107.034463   5289.124035  -4705.352207  -1757.073811 
##           442           443           444           445           446 
##   1982.012470  -3478.856001   3226.436943  -6250.070630 -11708.657485 
##           447           448           449           450           451 
##  -3969.037667  10208.104377  -1643.533244   5148.006249  -5563.662278 
##           452           453           454           455           456 
##   -749.497664    751.979517   3369.470838 -11983.208029   3810.009948 
##           457           458           459           460           461 
##  -6334.289485   6959.919051   3333.819078   2773.433148  -3621.166134 
##           462           463           464           465 
##   2366.496055    229.594087   2025.750381   -316.458664 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17195.35 20075.04 24377.59 24092.34 26472.58 23774.77 24499.27 19676.95 
##       10       11       12       13       14       15       16       17 
## 19410.50 16722.92 17509.26 14201.31 14253.06 14925.30 16641.25 14942.09 
##       18       19       20       21       22       23       24       25 
## 15988.57 15354.95 22518.26 21591.81 21065.73 22977.32 22295.61 22955.61 
##       26       27       28       29       30       31       32       33 
## 24822.64 18681.71 20427.57 28354.38 28412.69 28081.87 25684.21 27102.66 
##       34       35       36       37       38       39       40       41 
## 30989.58 31341.98 32767.25 30249.43 34190.43 37435.14 34457.63 31232.11 
##       42       43       44       45       46       47       48       49 
## 30066.73 20538.71 28143.28 30607.58 31709.21 38625.39 38114.20 42829.61 
##       50       51       52       53       54       55       56       57 
## 47114.25 39728.79 34235.24 29201.12 22267.28 28629.09 25170.69 21426.03 
##       58       59       60       61       62       63       64       65 
## 25884.71 27158.64 27459.04 27875.56 23699.36 40480.05 42345.06 37536.75 
##       66       67       68       69       70       71       72       73 
## 41791.72 46763.48 57553.44 55544.54 40619.27 38096.65 41149.01 35384.08 
##       74       75       76       77       78       79       80       81 
## 30784.41 21381.72 24619.75 20498.95 22608.82 17446.09 19484.52 18702.22 
##       82       83       84       85       86       87       88       89 
## 17718.85 15765.73 17058.09 20707.33 25175.49 26176.66 26201.90 26825.67 
##       90       91       92       93       94       95       96       97 
## 30997.84 29815.05 30825.81 28868.15 28058.83 28431.18 28842.40 22346.54 
##       98       99      100      101      102      103      104      105 
## 25395.99 18346.91 17189.99 15208.35 15511.53 16196.65 20720.33 19793.38 
##      106      107      108      109      110      111      112      113 
## 23344.55 23132.13 24827.38 27736.98 25206.54 21609.42 21887.99 24564.46 
##      114      115      116      117      118      119      120      121 
## 35552.96 33725.18 35583.63 38616.17 40594.40 38256.45 33018.36 29318.00 
##      122      123      124      125      126      127      128      129 
## 31424.59 29684.91 30879.96 38566.44 38204.82 37255.66 34083.39 35884.92 
##      130      131      132      133      134      135      136      137 
## 41344.40 40778.30 31879.61 33158.84 36385.00 32754.69 31123.56 30198.12 
##      138      139      140      141      142      143      144      145 
## 26715.86 28146.43 27913.43 25573.33 27620.85 26229.92 19759.05 22824.15 
##      146      147      148      149      150      151      152      153 
## 20626.05 23637.13 24183.28 25791.81 25975.97 27649.00 28964.58 32031.33 
##      154      155      156      157      158      159      160      161 
## 27449.83 26704.38 24231.75 30193.07 41162.65 39473.84 36808.73 41886.27 
##      162      163      164      165      166      167      168      169 
## 43310.90 46765.77 42179.98 37433.64 42920.76 59408.87 61646.23 60042.24 
##      170      171      172      173      174      175      176      177 
## 56831.73 55212.72 57946.12 56959.09 49163.30 52019.00 55804.31 55840.91 
##      178      179      180      181      182      183      184      185 
## 63051.05 53445.02 50158.91 40862.76 32305.61 35853.88 46072.57 45522.38 
##      186      187      188      189      190      191      192      193 
## 51543.03 57353.52 68259.48 73602.58 67225.36 67421.00 74571.94 70199.08 
##      194      195      196      197      198      199      200      201 
## 65670.85 54792.03 48754.09 50194.63 45739.10 37818.61 44316.61 42523.80 
##      202      203      204      205      206      207      208      209 
## 42157.64 42641.43 49512.92 58503.50 58128.89 59873.36 61548.03 65382.44 
##      210      211      212      213      214      215      216      217 
## 74956.91 66949.45 55003.83 49551.17 40444.84 37367.88 40517.04 30423.43 
##      218      219      220      221      222      223      224      225 
## 47590.48 54994.74 55906.93 78931.92 86512.39 88538.45 96216.36 87064.09 
##      226      227      228      229      230      231      232      233 
## 80994.09 80571.26 77183.03 76334.97 81125.99 82505.82 76877.84 72036.20 
##      234      235      236      237      238      239      240      241 
## 77754.30 64249.88 56195.59 48054.46 39570.40 43810.10 45987.37 39363.40 
##      242      243      244      245      246      247      248      249 
## 32970.91 43370.60 37551.90 41532.47 33708.16 32399.27 36096.22 38952.30 
##      250      251      252      253      254      255      256      257 
## 29677.23 35682.98 39527.74 44781.73 47531.03 47017.83 57436.20 75132.86 
##      258      259      260      261      262      263      264      265 
## 74945.80 68180.21 69678.93 65856.64 67319.27 61005.80 50158.15 46140.22 
##      266      267      268      269      270      271      272      273 
## 46352.02 42414.37 51318.86 47549.46 51743.47 49838.57 53952.30 54251.69 
##      274      275      276      277      278      279      280      281 
## 60337.72 57944.98 67728.22 61583.75 61796.18 60126.43 65934.77 59593.78 
##      282      283      284      285      286      287      288      289 
## 56118.47 45551.67 43930.13 61358.83 66951.97 67366.10 64754.25 63831.21 
##      290      291      292      293      294      295      296      297 
## 67877.79 71832.61 52612.48 42601.67 36544.42 46983.49 50262.29 49366.13 
##      298      299      300      301      302      303      304      305 
## 73838.36 79851.56 80526.83 85191.88 83369.91 78343.49 81849.86 56462.42 
##      306      307      308      309      310      311      312      313 
## 52680.89 52357.02 46076.36 43253.97 46898.42 39365.67 38073.19 32572.69 
##      314      315      316      317      318      319      320      321 
## 36400.41 35572.41 39447.59 37409.02 63489.53 61306.07 62948.18 71037.56 
##      322      323      324      325      326      327      328      329 
## 73452.13 99224.53 97583.69 73138.09 71922.23 70260.80 62120.69 59210.08 
##      330      331      332      333      334      335      336      337 
## 28815.53 32545.17 32989.72 35336.13 34669.44 40502.27 41590.16 36783.48 
##      338      339      340      341      342      343      344      345 
## 35988.93 36117.05 31382.57 37450.05 38121.03 38382.43 39268.23 41074.55 
##      346      347      348      349      350      351      352      353 
## 42917.35 42666.17 35530.25 25990.37 31396.08 30244.08 29829.45 27418.66 
##      354      355      356      357      358      359      360      361 
## 32150.99 35948.17 40462.77 38631.08 39905.76 42068.65 49548.39 50105.27 
##      362      363      364      365      366      367      368      369 
## 50309.24 52800.55 50255.22 49693.42 42254.57 39419.31 35552.60 33305.90 
##      370      371      372      373      374      375      376      377 
## 29318.64 36689.83 39002.92 46978.57 40881.31 40322.16 38842.75 38370.18 
##      378      379      380      381      382      383      384      385 
## 29133.47 33784.14 26756.39 35069.68 45520.87 49127.07 47380.71 49395.16 
##      386      387      388      389      390      391      392      393 
## 55686.42 65280.33 58414.01 52818.98 52544.58 60008.54 60537.90 69305.70 
##      394      395      396      397      398      399      400      401 
## 58286.79 59871.59 59426.67 58904.63 57374.22 56121.62 42728.78 51412.65 
##      402      403      404      405      406      407      408      409 
## 50401.63 49356.03 55830.56 48287.71 47590.99 45898.52 41526.29 40343.62 
##      410      411      412      413      414      415      416      417 
## 38385.33 32411.89 40374.79 43334.63 37945.96 32977.98 48019.88 51908.69 
##      418      419      420      421      422      423      424      425 
## 55882.94 48265.88 44547.09 43207.90 46835.44 35121.94 34848.04 29041.08 
##      426      427      428      429      430      431      432      433 
## 34694.94 43117.91 50088.99 46817.39 43852.50 40742.47 40629.38 37066.84 
##      434      435      436      437      438      439      440      441 
## 33161.00 30363.35 31957.50 33828.06 31809.82 36731.73 43008.35 39723.50 
##      442      443      444      445      446      447      448      449 
## 39426.13 42467.00 40328.85 44364.07 39556.51 30486.04 29310.18 40797.25 
##      450      451      452      453      454      455      456      457 
## 40475.14 46191.09 41777.21 42130.88 43769.96 47530.78 37288.99 42193.86 
##      458      459      460      461      462      463      464      465 
## 37564.65 45220.47 48780.85 51431.45 48123.50 50491.12 50694.96 52462.03 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8582
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    4.622095  0.5638473    2.909791
## t2* 1628.399814 30.3099002  246.683205
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.313797       4.739932   10.55653
## 2    lag_depvar 1287.404308    1638.559457 2091.10531

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Jul 11 00:36:17 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jul 11 00:36:24 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jul 11 00:36:31 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Jul 11 00:36:38 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Jul 11 00:36:45 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jul 11 00:36:52 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jul 11 00:36:58 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Jul 11 00:37:05 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Jul 11 00:37:12 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Jul 11 00:37:19 2022
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3) %>% 
  knitr::kable(format="html", caption="Tabla. Gastos promedio por ítem a contar del...",
               col.names= c("Item","2023","2022","2021","2020")) %>% 
  kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
  kableExtra::scroll_box(width = "100%", height = "375px")
Tabla. Gastos promedio por ítem a contar del…
Item 2023 2022 2021 2020
Agua NA 7.328667 7.328667 7.780500
Comida NA 304.551500 304.551500 345.242400
Comunicaciones NA 0.000000 0.000000 0.000000
Electricidad NA 37.112000 37.112000 27.473433
Enceres NA 10.915000 10.915000 23.708267
Farmacia NA 3.663333 3.663333 11.945800
Gas/Bencina NA 54.006667 54.006667 23.138133
Diosi NA 13.517833 13.517833 38.627233
donaciones/regalos NA 0.000000 0.000000 9.157300
Electrodomésticos/ Mantención casa NA 7.888000 7.888000 27.648933
VTR NA 28.990000 28.990000 21.078267
Netflix NA 7.369500 7.369500 7.584300
Otros NA 6.302167 6.302167 1.260433
Total 0 481.644667 481.644667 544.645000
## Joining, by = "word"


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: 35 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:1651, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2022-07-09 00:04:58 sería de: 34.576 pesos// Percentil 95% más alto proyectado: 37.593,51

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)

dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="html", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)")) %>% 
  kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
  kableExtra::scroll_box(width = "100%", height = "375px")
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 33418.81 33372.22
Lo.80 33576.76 33604.30
Point.Forecast 34576.35 37139.05
Hi.80 36243.66 41866.86
Hi.95 37158.59 44369.61


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1      mean
##       0.3280  985.7562
## s.e.  0.1535   38.4750
## 
## sigma^2 = 29191:  log likelihood = -267.98
## AIC=541.96   AICc=542.61   BIC=547.1
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1     xreg
##       0.3560  33.7281
## s.e.  0.1481   1.3248
## 
## sigma^2 = 27491:  log likelihood = -266.76
## AIC=539.52   AICc=540.17   BIC=544.67
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="html", caption="Tabla. Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) %>% 
  kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
  kableExtra::scroll_box(width = "100%", height = "375px")
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 905.6835 631.2725 664.1008
Lo.80 1026.0534 753.9717 743.6058
Point.Forecast 1253.4378 985.7558 920.6852
Hi.80 1480.8221 1217.5400 1219.2415
Hi.95 1601.1921 1340.2391 1414.6897


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.2.7  bsts_0.9.8          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.10         MASS_7.3-54         scales_1.2.0       
##  [7] ggiraph_0.8.2       tidytext_0.3.3      DT_0.23            
## [10] autoplotly_0.1.4    rvest_1.0.2         plotly_4.10.0      
## [13] xts_0.12.1          forecast_8.16       wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.0     tm_0.7-8           
## [19] NLP_0.2-1           tsibble_1.1.1       forcats_0.5.1      
## [22] dplyr_1.0.9         purrr_0.3.4         tidyr_1.2.0        
## [25] tibble_3.1.7        ggplot2_3.3.6       tidyverse_1.3.1    
## [28] sjPlot_2.8.10       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-2       sparklyr_1.7.7      httr_1.4.3         
## [34] readxl_1.4.0        zoo_1.8-10          stringr_1.4.0      
## [37] stringi_1.7.6       DataExplorer_0.8.2  data.table_1.14.2  
## [40] reshape2_1.4.4      fUnitRoots_3042.79  fBasics_3042.89.2  
## [43] timeSeries_3062.100 timeDate_3043.102   plyr_1.8.7         
## [46] readr_2.1.2        
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2         tidyselect_1.1.2   lme4_1.1-30       
##   [4] htmlwidgets_1.5.4  munsell_0.5.0      codetools_0.2-18  
##   [7] effectsize_0.7.0   its.analysis_1.6.0 withr_2.5.0       
##  [10] colorspace_2.0-3   ggfortify_0.4.14   highr_0.9         
##  [13] knitr_1.39         uuid_1.1-0         rstudioapi_0.13   
##  [16] TTR_0.24.3         labeling_0.4.2     emmeans_1.7.5     
##  [19] slam_0.1-50        bit64_4.0.5        farver_2.1.1      
##  [22] datawizard_0.4.1   rprojroot_2.0.3    vctrs_0.4.1       
##  [25] generics_0.1.3     xfun_0.31          R6_2.5.1          
##  [28] bitops_1.0-7       assertthat_0.2.1   networkD3_0.4     
##  [31] vroom_1.5.7        nnet_7.3-16        gtable_0.3.0      
##  [34] spatial_7.3-14     rlang_1.0.3        forge_0.2.0       
##  [37] systemfonts_1.0.4  splines_4.1.2      lazyeval_0.2.2    
##  [40] selectr_0.4-2      broom_1.0.0        yaml_2.3.5        
##  [43] abind_1.4-5        modelr_0.1.8       crosstalk_1.2.0   
##  [46] backports_1.4.1    quantmod_0.4.20    tokenizers_0.2.1  
##  [49] tools_4.1.2        ellipsis_0.3.2     gplots_3.1.3      
##  [52] kableExtra_1.3.4   jquerylib_0.1.4    Rcpp_1.0.9        
##  [55] base64enc_0.1-3    fracdiff_1.5-1     haven_2.5.0       
##  [58] fs_1.5.2           magrittr_2.0.3     lmtest_0.9-40     
##  [61] reprex_2.0.1       mvtnorm_1.1-3      sjmisc_2.8.9      
##  [64] hms_1.1.1          evaluate_0.15      xtable_1.8-4      
##  [67] sjstats_0.18.1     ggeffects_1.1.2    compiler_4.1.2    
##  [70] KernSmooth_2.23-20 crayon_1.5.1       minqa_1.2.4       
##  [73] htmltools_0.5.2    tzdb_0.3.0         lubridate_1.8.0   
##  [76] DBI_1.1.3          sjlabelled_1.2.0   dbplyr_2.2.1      
##  [79] boot_1.3-28        Matrix_1.3-4       car_3.1-0         
##  [82] cli_3.3.0          quadprog_1.5-8     parallel_4.1.2    
##  [85] insight_0.18.0     igraph_1.3.2       pkgconfig_2.0.3   
##  [88] xml2_1.3.3         svglite_2.1.0      bslib_0.3.1       
##  [91] webshot_0.5.3      estimability_1.4   anytime_0.3.9     
##  [94] snakecase_0.11.0   janeaustenr_0.1.5  digest_0.6.29     
##  [97] parameters_0.18.1  janitor_2.1.0      rmarkdown_2.14    
## [100] cellranger_1.1.0   curl_4.3.2         gtools_3.9.2.2    
## [103] urca_1.3-0         nloptr_2.0.3       lifecycle_1.0.1   
## [106] nlme_3.1-153       jsonlite_1.8.0     tseries_0.10-51   
## [109] carData_3.0-5      viridisLite_0.4.0  fansi_1.0.3       
## [112] pillar_1.7.0       fastmap_1.1.0      glue_1.6.2        
## [115] bayestestR_0.12.1  bit_4.0.4          sass_0.4.1        
## [118] performance_0.9.1  r2d3_0.2.6         caTools_1.18.2
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Paquetes estadísticos utilizados')),
      options=list(
initComplete = htmlwidgets::JS(
      "function(settings, json) {",
      "$(this.api().tables().body()).css({'font-size': '80%'});",
      "}")))